Best Streaming Analytics Platforms

Compare the Top Streaming Analytics Platforms as of December 2025

What are Streaming Analytics Platforms?

Streaming analytics platforms are software solutions that enable real-time processing and analysis of data as it is generated or streamed from various sources such as IoT devices, sensors, social media, and transactional systems. These platforms allow businesses to gain immediate insights from continuous data streams, enabling them to make faster decisions, detect anomalies, and optimize operations in real-time. Key features of streaming analytics platforms include data ingestion, real-time event processing, pattern recognition, and advanced analytics like predictive modeling and machine learning integration. They are commonly used in applications such as fraud detection, customer behavior analysis, network monitoring, and supply chain optimization. Compare and read user reviews of the best Streaming Analytics platforms currently available using the table below. This list is updated regularly.

  • 1
    StarTree

    StarTree

    StarTree

    StarTree, powered by Apache Pinot™, is a fully managed real-time analytics platform built for customer-facing applications that demand instant insights on the freshest data. Unlike traditional data warehouses or OLTP databases—optimized for back-office reporting or transactions—StarTree is engineered for real-time OLAP at true scale, meaning: - Data Volume: query performance sustained at petabyte scale - Ingest Rates: millions of events per second, continuously indexed for freshness - Concurrency: thousands to millions of simultaneous users served with sub-second latency With StarTree, businesses deliver always-fresh insights at interactive speed, enabling applications that personalize, monitor, and act in real time.
    Starting Price: Free
  • 2
    Materialize

    Materialize

    Materialize

    Materialize is a reactive database that delivers incremental view updates. We help developers easily build with streaming data using standard SQL. Materialize can connect to many different external sources of data without pre-processing. Connect directly to streaming sources like Kafka, Postgres databases, CDC, or historical sources of data like files or S3. Materialize allows you to query, join, and transform data sources in standard SQL - and presents the results as incrementally-updated Materialized views. Queries are maintained and continually updated as new data streams in. With incrementally-updated views, developers can easily build data visualizations or real-time applications. Building with streaming data can be as simple as writing a few lines of SQL.
    Starting Price: $0.98 per hour
  • 3
    CelerData Cloud
    CelerData is a high-performance SQL engine built to power analytics directly on data lakehouses, eliminating the need for traditional data‐warehouse ingestion pipelines. It delivers sub-second query performance at scale, supports on-the‐fly JOINs without costly denormalization, and simplifies architecture by allowing users to run demanding workloads on open format tables. Built on the open source engine StarRocks, the platform outperforms legacy query engines like Trino, ClickHouse, and Apache Druid in latency, concurrency, and cost-efficiency. With a cloud-managed service that runs in your own VPC, you retain infrastructure control and data ownership while CelerData handles maintenance and optimization. The platform is positioned to power real-time OLAP, business intelligence, and customer-facing analytics use cases and is trusted by enterprise customers (including names such as Pinterest, Coinbase, and Fanatics) who have achieved significant latency reductions and cost savings.
  • 4
    Kinetica

    Kinetica

    Kinetica

    A scalable cloud database for real-time analysis on large and streaming datasets. Kinetica is designed to harness modern vectorized processors to be orders of magnitude faster and more efficient for real-time spatial and temporal workloads. Track and gain intelligence from billions of moving objects in real-time. Vectorization unlocks new levels of performance for analytics on spatial and time series data at scale. Ingest and query at the same time to act on real-time events. Kinetica's lockless architecture and distributed ingestion ensures data is available to query as soon as it lands. Vectorized processing enables you to do more with less. More power allows for simpler data structures, which lead to lower storage costs, more flexibility and less time engineering your data. Vectorized processing opens the door to amazingly fast analytics and detailed visualization of moving objects at scale.
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